Welcome to www.adaptics.com

IMAGINE the POSSIBILITIES

DATA IN -- MODEL OUT

ADAPTx

Automated Multivariable System Identification

and Time Series Analysis Software

Automated

Multivariable Systems

Stable Computation

State Order Selection

Unknown Feedback

Model Accuracy

Introducing ADAPTx, the next generation software for modeling
complex dynamical systems from measured data. ADAPTx automatically determines
optimal statistical models in a robust and stable computation. The accuracy of
the fitted model is described by computed accuracy bounds. This complete
procedure is entirely automated, or you may wish to use the interactive menus to
investigate the effect of making choices other than those considered
statistically optimal by ADAPTx.

Completely Automatic. Supply the measured data and ADAPTx
identifies a state space model.

General Model Class. These systems involve multiple inputs and
outputs, high state order, stiff dynamics, unstable dynamics, unknown feedback,
colored state and measurement noise, bias and trends in the measurements.
ADAPTx applies to general linear stochastic systems with no assumed prior
structural form.

Stable Computation. ADAPTx computation involves primarily the
singular value decomposition which is always stable and accurate. The state
space model description is always well conditioned. No use is made of iterative
parameter optimization that may not converge. Required computation is
predetermined by the problem size.

Optimal Model Fitting. The model fitting is close to the optimum
achievable accuracy. The model state order is chosen to minimize the
statistically optimal Akaike information criterion (AIC) .
This optimality is approximated even for small samples, and no initialization
or prior information is used.

Identified Model Accuracy. Accuracy is given by confidence bands on
the transfer function and power spectrum estimates, and on
maximum singular value quantities. These can be used directly in robust control
design.

Conference Sessions. The Invited Industry Applications
Session, Tutorial: Automated Multivariable System Identification,
will be presented at the 1999 American Control Conference, Thursday Afternoon,
June 3, 1999, San Diego, CA